Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

The article examines the territory of East Kazakhstan, where a sharply continental climate prevails with hot summers, cold and snowy winters. The mountainous regions of East Kazakhstan are represented by the Kalba, Altai and Saur-Tarbagatay ranges, they are surrounded by rolling plains. The highest points are at 3000-4500 m. On average, the heights are in the range of 900-1400 m. Despite the low heights in the mountainous area, the problem of avalanche safety is acute in the region. At the same time, the situation is complicated by not always predictable weather events, the frequency of which is increasing every year. These include heavy precipitation, sometimes combined with a sharp warming in winter, and the changing wind regime of the territory. To identify regional climate changes and its connection with the avalanche-prone situation in the region, the study analyzed meteorological data from weather stations located directly near avalanche prone locations over the past 23 years since 2001, as well as data from observations of avalanche-prone areas since 2005 and information on registered spontaneous avalanches from 2013 to the present. This study is the first in the East Kazakhstan region, which presents the results of a comprehensive analysis of data on 497 avalanche-prone sites, of which 325 sites pose a threat to life and infrastructure. 10 most dangerous sites have been selected for detailed study. The analysis of climate data was carried out based on information from 7 weather stations. The article discusses the main climatic changes in the region, including an increase in air temperature, an increase in precipitation and a change in wind conditions. Data from weather stations and snow measuring routes covering the period since 2005 have been used to develop probabilistic avalanche forecasts. The use of statistical methods and the analysis of the relationships between meteorological parameters (temperature, precipitation, wind speed) made it possible to build models predicting avalanche-prone situations. Data on spontaneous avalanches were analyzed for five sites (Bogatyrevskaya site, Prokhodnaya, Sogornoye-Barlyk, Tainty and Pikhtovka). Based on these data, equations of dependence of temperature, wind and precipitation have been developed, which improves the accuracy of forecasting avalanche risks. An analysis of the data in the Statistica program showed a significant relationship between sudden warming, increased wind speed and precipitation, which precedes avalanches. Regression equations and the approximation confidence coefficient for the average values of the studied parameters are obtained. The results of the study make it possible not only to establish patterns, but also to propose effective methods for monitoring and forecasting avalanche hazard in the region. According to the data analysis, regional features of climate change in East Kazakhstan were identified, and a comparison was made with previously known works on Kazakhstan. The interrelation of climatic characteristics with avalanche hazard in the region is shown. The results obtained in the study will help us to better understand the regional manifestations of climate change. An important task for further forecasting of avalanche activity is the correct design of the avalanche collection database. The authors identified information objects (entities). An ontological database model (Entity Relationship Diagram) is constructed. Based on it, a database has been created for a system for monitoring and forecasting avalanche activity in the East Kazakhstan region.

Avalanche hazard prediction remains a crucial task for mountainous regions worldwide. This study presents the development and field testing of a prototype automated avalanche hazard monitoring system designed for the East Kazakhstan region. The system integrates a snow avalanche station (including temperature, humidity, and pressure sensors; a magnetoelectric wind sensor; a data logger; and devices for autonomous operation), a temperature snow measuring rod, an API (application programming interface) service for storing weather and climate parameters in a database, and a web interface. Powered by autonomous solar energy solutions, the system ensures continuous, high-resolution monitoring of key environmental parameters. Using initial test datasets, we analyzed the specific strengths and weaknesses of the developed monitoring system using the example of one avalanche site. Avalanche prediction was performed using regression analysis (logistic regression). The evaluation of the model showed a high forecasting accuracy, with recognition rates exceeding 98%. The obtained regression coefficients can be used to predict avalanches based on meteorological data collected using the proposed equipment. The developed solution holds significant promise for improving avalanche risk management practices and can be expanded for broader application in both national and international contexts.

This article discusses common approaches to the development of ANPR systems. The main idea of the article is the parallel use of several different classifiers for OCR and fusion of obtained data for improving the recognition accuracy.

This article considers the relevant problem of biometric authentication of students in higher educational institutions. The authors present the results of using a turnstile system with a face recognition terminal, with the ability to provide unique biometric data in real time. The study was conducted among students of the D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan. The article presents the results of studies of one of the biometric methods of personality recognition. In this method, the process of proving and verifying the identity of the person can be carried out through the presentation by the user of his biometric image. The processing results are sorted and compared with typical images from the database. With its positive decision, the developed software issues the results of biometric authentication of a person who presented himself in front of a digital scanner. The applied value of the results of the work lies in the possibility of using them in the field of education, and various industries to make a decision on providing access to information resources. In the course of the study, a technology was developed to provide biometric authentication processes for university students. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted. A review of biometric methods of recognition is carried out, and tools for electronic authentication and modern information security systems are described. Factors that significantly affect the probability of correct recognition of students’ faces are determined. The analysis of ways to increase the probability of correct recognition of students by the image of the face is carried out.

The formation of avalanche hazards in mountainous regions is largely influenced by slope insolation and albedo. This paper presents a quantitative analysis of how solar radiation, surface reflectivity (albedo), temperature, and snow cover affect avalanche formation depending on slope aspect (north-, south-, east-, and west-facing). This study is based on remote sensing data from MODIS, ERA5-Land, CHIRPS, and a digital terrain model for the winter periods from 2000 to 2024. The results show that north-facing slopes have higher albedo values (up to 0.95) and greater snow cover stability (30–50%), which contributes to increased avalanche risk, especially at temperatures above −5 °C. South-facing slopes are characterized by lower albedo values (around 0.20–0.40) and more intense snowmelt, which reduces the likelihood of avalanches. Regression analysis revealed a strong positive correlation between snow depth and avalanche risk (r = 0.87), as well as a moderate negative correlation between temperature and snow cover stability (r = −0.25). The influence of albedo on avalanche risk was found to be indirect, acting through its impact on the surface energy balance. The resulting avalanche risk map demonstrated high accuracy (overall agreement: 86%; Kappa coefficient: 0.72), highlighting the effectiveness of an integrated approach based on geophysical and climatic parameters. The data obtained can be used to support avalanche safety management and slope assessment in the context of climate change.